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  "cells": [
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/SahilCarterr/FinetuningLLM/blob/main/gemma_7B_finetuned.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Installing Libraries"
      ],
      "metadata": {
        "id": "A1r-8aSyVe-8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "-dXfXLC4sQc9",
        "outputId": "a865bb39-f682-4f5f-9360-96f0af0f67f4"
      },
      "outputs": [
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          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m103.4/103.4 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m27.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m20.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m18.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m222.7/222.7 MB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m245.2/245.2 kB\u001b[0m \u001b[31m23.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.6/251.6 kB\u001b[0m \u001b[31m30.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.4/309.4 kB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip -q install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "!pip -q install --no-deps xformers \"trl<0.9.0\" peft accelerate bitsandbytes"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Loading the model and tokenizer"
      ],
      "metadata": {
        "id": "owKPaUyKVmrK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 2048\n",
        "dtype = None\n",
        "load_in_4bit = True\n",
        "\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/gemma-7b-bnb-4bit\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415,
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        },
        "id": "WaCib9mTsaKt",
        "outputId": "8f5a8777-4363-457d-d934-cc1a57f81082"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.11k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e2f556651b140baabd5cbacc179924e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==((====))==  Unsloth: Fast Gemma patching release 2024.6\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.3.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. Xformers = 0.0.26.post1. FA = False.\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/5.57G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7df14b40a37f4f6697c1d656787dd5d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`config.hidden_act` is ignored, you should use `config.hidden_activation` instead.\n",
            "Gemma's activation function will be set to `gelu_pytorch_tanh`. Please, use\n",
            "`config.hidden_activation` if you want to override this behaviour.\n",
            "See https://github.com/huggingface/transformers/pull/29402 for more details.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/40.0k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/636 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/17.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2.1 Loading Peft Config"
      ],
      "metadata": {
        "id": "Z1249jc3VroI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 16,\n",
        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
        "    lora_alpha = 16,\n",
        "    lora_dropout = 0,\n",
        "    bias = \"none\",\n",
        "\n",
        "    use_gradient_checkpointing = \"unsloth\",\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,\n",
        "    loftq_config = None,\n",
        ")"
      ],
      "metadata": {
        "id": "oJY7DeuRsuWR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38a2efe4-4b08-441e-ce31-e4e917b6db63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2024.6 patched 28 layers with 28 QKV layers, 28 O layers and 28 MLP layers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2.2 Formatting prompt and initializing alpaca_prompt"
      ],
      "metadata": {
        "id": "fX6RkwmUV5s4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alpaca_prompt = \"\"\"Below is an question which corresponds to topic. Write a topic that appropriate for the question.\n",
        "\n",
        "### question:\n",
        "{}\n",
        "\n",
        "### topic:\n",
        "{}\"\"\"\n",
        "\n",
        "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
        "def formatting_prompts_func(examples):\n",
        "    question = examples[\"question\"]\n",
        "    topic = examples[\"topic\"]\n",
        "    texts = []\n",
        "    for question, topic in zip(question, topic):\n",
        "        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n",
        "        text = alpaca_prompt.format(question, topic) + EOS_TOKEN\n",
        "        texts.append(text)\n",
        "    return { \"text\" : texts, }\n",
        "pass\n"
      ],
      "metadata": {
        "id": "UKEG4PqPszUB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3. Preprocessing the data"
      ],
      "metadata": {
        "id": "Vhyz_pqeV_NB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/data_withoutLENE.csv')\n",
        "unique_topics = df['topic'].unique()\n",
        "topic_mapping = {topic: i for i, topic in enumerate(unique_topics)}\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\n",
        "\n",
        "# Save the train and test datasets to new CSV files\n",
        "train_data.to_csv('train.csv', index=False)\n",
        "test_data.to_csv('test.csv', index=False)\n",
        "\n",
        "print(\"Data has been split and saved to train.csv and test.csv.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PBUHfNvUuTmT",
        "outputId": "cfcb4c0e-154f-4b3b-a4ba-ff1fa6ea6e6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data has been split and saved to train.csv and test.csv.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip -q install pyarrow datasets"
      ],
      "metadata": {
        "id": "9sOrTyczumBG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3.1 Split and Load the dataset into Pytorch Dataloader"
      ],
      "metadata": {
        "id": "t18he-yDWOK3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "data_files = {\"train\": \"/content/train.csv\", \"test\": \"/content/test.csv\"}\n",
        "dataset = load_dataset(\"csv\", data_files=data_files)"
      ],
      "metadata": {
        "id": "mP-70t6yumde"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset[\"train\"]\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Psh3ZoNNwFZ_",
        "outputId": "e4d379ef-156f-4e1f-fbd4-07b2fe1fc288"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['question', 'topic'],\n",
              "    num_rows: 1274\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.map(formatting_prompts_func, batched = True,)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "0eb4182e2a0a49ccb4f684727b165e36",
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        },
        "id": "WwiuXllOupy_",
        "outputId": "b080b2f8-0e97-4dd6-f8bd-726b6a480a6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1274 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 4. Training model"
      ],
      "metadata": {
        "id": "wSm7dtaJWZvg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "from unsloth import is_bfloat16_supported\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    train_dataset = dataset,\n",
        "    dataset_text_field = \"text\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dataset_num_proc = 2,\n",
        "    packing = False,\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_steps = 5,\n",
        "        num_train_epochs=1,\n",
        "        learning_rate = 2e-4,\n",
        "        fp16 = not is_bfloat16_supported(),\n",
        "        bf16 = is_bfloat16_supported(),\n",
        "        logging_steps = 1,\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.01,\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        seed = 3407,\n",
        "        output_dir = \"outputs\",\n",
        "    ),\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "4726fd8af37a454c9bd8d5326d9181fe",
            "2e03307ece5a4ec0915a48cb22a59838",
            "537f969882fe4eefa89487e1044df0f9",
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            "696141b0702741f4b648bc3177d2c931",
            "f90de6eb5ce04b9eb51f415754a4c73e",
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            "620b9830ef6b4d1ca34a104d1a1781db"
          ]
        },
        "id": "hbA6kBtkvX_o",
        "outputId": "d2e17a2b-8b77-4bc4-d459-b7e5e71a280f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/1274 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4726fd8af37a454c9bd8d5326d9181fe"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer_stats = trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3f_JjDBev27N",
        "outputId": "cfc359bf-9ef4-4507-e8af-465d565ab5c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 1,274 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 8 | Total steps = 159\n",
            " \"-____-\"     Number of trainable parameters = 50,003,968\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='159' max='159' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [159/159 10:09, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>4.486300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>4.107500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>4.064300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>3.368400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.714300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.439600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.308900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.870900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.666200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.525100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.569500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.311400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.337000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.325300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.083000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>1.060300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>1.059100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>1.192000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.127000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.142200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.892400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>1.209500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>1.222200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>1.441700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.394700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.951500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>1.235600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.267800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>1.017300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.941400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.985000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.979000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.947100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>1.102500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.857900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.898000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.904300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>1.170600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>1.309700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.834700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.862100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.801400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.885400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.982400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.712600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.862400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.816200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.986200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>1.337400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.524300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.904300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.852000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>1.175200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.723600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.879400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>1.129300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>1.186500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.670700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.806500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.141400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.911600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.614100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.863900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>1.184800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.637900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>0.826500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>1.444000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>1.101200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>1.181000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.957500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>1.047100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>1.179600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>0.945200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>1.027700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>1.048700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>1.049700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>1.284400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.744200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>1.107000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.995300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>0.979000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>1.079700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>1.055300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>1.126400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>0.711700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>0.956200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>1.004700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>0.927700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>1.204400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.858800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>1.034100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>0.993300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>1.069700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>0.976900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.970300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>1.042800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>1.301000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>1.099500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>1.421500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.773400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>101</td>\n",
              "      <td>0.739500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>102</td>\n",
              "      <td>1.026200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>103</td>\n",
              "      <td>0.912400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>104</td>\n",
              "      <td>0.908200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>105</td>\n",
              "      <td>0.537600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>106</td>\n",
              "      <td>0.846200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>107</td>\n",
              "      <td>1.209600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>108</td>\n",
              "      <td>0.728700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>109</td>\n",
              "      <td>1.005500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>1.281700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>111</td>\n",
              "      <td>0.816700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>112</td>\n",
              "      <td>1.149700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>113</td>\n",
              "      <td>1.145300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>114</td>\n",
              "      <td>1.020200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>115</td>\n",
              "      <td>1.005100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>116</td>\n",
              "      <td>1.143100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>117</td>\n",
              "      <td>0.992500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>118</td>\n",
              "      <td>0.867200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>119</td>\n",
              "      <td>0.861100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.633400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>121</td>\n",
              "      <td>1.016600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>122</td>\n",
              "      <td>0.755900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123</td>\n",
              "      <td>1.241000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>124</td>\n",
              "      <td>0.642500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>0.879500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>126</td>\n",
              "      <td>0.965000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>127</td>\n",
              "      <td>0.916100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>128</td>\n",
              "      <td>0.940300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>129</td>\n",
              "      <td>1.037400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>1.031400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>131</td>\n",
              "      <td>0.977700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>132</td>\n",
              "      <td>0.897000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>133</td>\n",
              "      <td>1.125300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>134</td>\n",
              "      <td>1.068800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>135</td>\n",
              "      <td>1.108600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>136</td>\n",
              "      <td>0.857100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>137</td>\n",
              "      <td>0.823400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>138</td>\n",
              "      <td>1.521500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>139</td>\n",
              "      <td>0.678700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>1.360600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>141</td>\n",
              "      <td>0.976200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>142</td>\n",
              "      <td>0.959300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>143</td>\n",
              "      <td>0.687700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>144</td>\n",
              "      <td>1.015400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>145</td>\n",
              "      <td>1.345400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>146</td>\n",
              "      <td>1.129200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>147</td>\n",
              "      <td>0.996200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>148</td>\n",
              "      <td>0.997800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>149</td>\n",
              "      <td>1.045600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.007900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>151</td>\n",
              "      <td>0.898900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>152</td>\n",
              "      <td>1.010400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>153</td>\n",
              "      <td>0.842900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>154</td>\n",
              "      <td>1.002100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>155</td>\n",
              "      <td>1.052300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>156</td>\n",
              "      <td>1.045600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>157</td>\n",
              "      <td>1.270400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>158</td>\n",
              "      <td>0.853400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>159</td>\n",
              "      <td>0.943300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 5. Inference on single question"
      ],
      "metadata": {
        "id": "EnXZtveYWk3P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "FastLanguageModel.for_inference(model)\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_prompt.format(\n",
        "        dataset[\"question\"][2],#question\n",
        "        \"\", # topic - for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 20, use_cache = True)\n",
        "a=tokenizer.batch_decode(outputs)"
      ],
      "metadata": {
        "id": "F1z-vmDMv23N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "pattern = r'### topic:\\n(.+)<'\n",
        "\n",
        "# Search for the pattern in the text\n",
        "match = re.search(pattern, a[0])\n",
        "\n",
        "if match:\n",
        "    topic = match.group(1)\n",
        "    print(f\"{topic}\")\n",
        "    if(topic==dataset[\"topic\"][2]):\n",
        "      print(\"Correct\")\n",
        "    else:\n",
        "      print(\"Incorrect\")\n",
        "else:\n",
        "    print(\"No topic found.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-wYWchLB9SO-",
        "outputId": "ae7fcdd9-a002-4c11-9e61-f42fac98cc92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Finance\n",
            "Correct\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 4.1 Inferene on test dataset"
      ],
      "metadata": {
        "id": "kC8OeHHLWwR4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test= dataset[\"test\"]"
      ],
      "metadata": {
        "id": "YkVbsvO70Kax"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test['question'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "bV1t0wQb0mSo",
        "outputId": "fce38baa-1104-4592-d3c7-7cdc2c2fd7c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Guess the name of the girl'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(dataset_test['question'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JaXhPjgo3gF9",
        "outputId": "e5a05cc9-3384-403a-ef41-7437f6aee845"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "319"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8xUTFYNT_Hwv",
        "outputId": "bdca8482-4e43-44b2-8593-85b147607b34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['question', 'topic'],\n",
              "    num_rows: 319\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c=0\n",
        "for i in range(len(dataset_test['question'])):\n",
        "\n",
        "  FastLanguageModel.for_inference(model)\n",
        "  inputs = tokenizer(\n",
        "  [\n",
        "      alpaca_prompt.format(\n",
        "          dataset_test['question'][i],#question\n",
        "          \"\", # topic - for generation!\n",
        "      )\n",
        "  ], return_tensors = \"pt\").to(\"cuda\")\n",
        "  outputs = model.generate(**inputs, max_new_tokens = 20, use_cache = True)\n",
        "  a=tokenizer.batch_decode(outputs)\n",
        "  import re\n",
        "  pattern = r'### topic:\\n(.+)<'\n",
        "\n",
        "  # Search for the pattern in the text\n",
        "  match = re.search(pattern, a[0])\n",
        "\n",
        "  if match:\n",
        "      topic = match.group(1)\n",
        "      #print(f\"{topic}\")\n",
        "      if(topic==dataset_test[\"topic\"][i]):\n",
        "        #print(\"Correct\")\n",
        "        c+=1\n",
        "      else:\n",
        "        print(\"Incorrect\")"
      ],
      "metadata": {
        "id": "5mjp_UIT3NVx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YJaPe8nD6P9Y",
        "outputId": "3aa90201-56c3-40ab-c7aa-556eb9c427e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "246"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 6. Computing Metrics"
      ],
      "metadata": {
        "id": "22rHTqQeW7J1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Accuracy is {c/len(dataset_test['question'])}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZMxiVgDA1_wr",
        "outputId": "0686e073-6850-4305-fea6-eeb82eeeb69f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy is 0.7711598746081505\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "classes = ['Dating and Relationships', 'Random', 'Movies and TV', 'Music', 'Technology and Internet', 'Philosophy and Religion', 'technology and Internet', 'Law and Order', 'Health and Wellness', 'Food and Drinks', 'Fashion and Makeup', 'health and Wellness', 'Gaming', 'Sports', 'fashion and Makeup', 'sports', 'Anime', 'anime', 'Finance', 'Travel and Geography', 'Pets and Animals', 'gaming', 'random', 'music']\n",
        "\n",
        "# Convert words to class indices\n",
        "def word_to_class_index(word):\n",
        "    return classes.index(word)\n",
        "\n",
        "y_true = [word_to_class_index(word) for word in y_true_words]\n",
        "y_pred = [word_to_class_index(word) for word in y_pred_words]\n",
        "\n",
        "# Initialize dictionaries to store counts for each class\n",
        "class_counts = defaultdict(lambda: {'TP': 0, 'FP': 0, 'FN': 0})\n",
        "\n",
        "# Calculate TP, FP, FN for each class\n",
        "for true, pred in zip(y_true, y_pred):\n",
        "    if true == pred:\n",
        "        class_counts[true]['TP'] += 1\n",
        "    else:\n",
        "        class_counts[true]['FN'] += 1\n",
        "        class_counts[pred]['FP'] += 1\n",
        "\n",
        "# Calculate Precision and Recall for each class\n",
        "class_metrics = {}\n",
        "for cls in classes:\n",
        "    TP = class_counts[classes.index(cls)]['TP']\n",
        "    FP = class_counts[classes.index(cls)]['FP']\n",
        "    FN = class_counts[classes.index(cls)]['FN']\n",
        "\n",
        "    precision = TP / (TP + FP) if (TP + FP) != 0 else 0\n",
        "    recall = TP / (TP + FN) if (TP + FN) != 0 else 0\n",
        "\n",
        "    class_metrics[cls] = {'Precision': precision, 'Recall': recall}\n",
        "\n",
        "# Print results\n",
        "for cls, metrics in class_metrics.items():\n",
        "    print(f'Class: {cls}')\n",
        "    print(f'Precision: {metrics[\"Precision\"]:.2f}')\n",
        "    print(f'Recall: {metrics[\"Recall\"]:.2f}')\n",
        "    print()"
      ],
      "metadata": {
        "id": "yH_X1dQB_28n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9835e87-ef26-4c09-d60c-dbc276373cfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class: Dating and Relationships\n",
            "Precision: 0.33\n",
            "Recall: 0.2\n",
            "\n",
            "Class: Random\n",
            "Precision: 0.7\n",
            "Recall: 0.83\n",
            "\n",
            "Class: Movies and TV\n",
            "Precision: 0.72\n",
            "Recall: 0.81\n",
            "\n",
            "Class: Music\n",
            "Precision: 0.82\n",
            "Recall: 0.9\n",
            "\n",
            "Class: Technology and Internet\n",
            "Precision: 0.33\n",
            "Recall: 0.18\n",
            "\n",
            "Class: Philosophy and Religion\n",
            "Precision: 0.0\n",
            "Recall: 0.0\n",
            "\n",
            "Class: technology and Internet\n",
            "Precision: 0.0\n",
            "Recall: 0.0\n",
            "\n",
            "Class: Law and Order\n",
            "Precision: 0.79\n",
            "Recall: 0.85\n",
            "\n",
            "Class: Health and Wellness\n",
            "Precision: 0.31\n",
            "Recall: 0.62\n",
            "\n",
            "Class: Food and Drinks\n",
            "Precision: 0.89\n",
            "Recall: 0.62\n",
            "\n",
            "Class: Fashion and Makeup\n",
            "Precision: 0.89\n",
            "Recall: 0.73\n",
            "\n",
            "Class: health and Wellness\n",
            "Precision: 0.0\n",
            "Recall: 0.0\n",
            "\n",
            "Class: Gaming\n",
            "Precision: 1.0\n",
            "Recall: 0.82\n",
            "\n",
            "Class: Sports\n",
            "Precision: 0.9\n",
            "Recall: 0.95\n",
            "\n",
            "Class: fashion and Makeup\n",
            "Precision: 0.0\n",
            "Recall: 0.0\n",
            "\n",
            "Class: sports\n",
            "Precision: 0.0\n",
            "Recall: 0.0\n",
            "\n",
            "Class: Anime\n",
            "Precision: 0.82\n",
            "Recall: 0.88\n",
            "\n",
            "Class: anime\n",
            "Precision: 0.0\n",
            "Recall: 0.0\n",
            "\n",
            "Class: Finance\n",
            "Precision: 1.0\n",
            "Recall: 0.88\n",
            "\n",
            "Class: Travel and Geography\n",
            "Precision: 0.83\n",
            "Recall: 0.56\n",
            "\n",
            "Class: Pets and Animals\n",
            "Precision: 0.76\n",
            "Recall: 1.0\n",
            "\n",
            "Class: gaming\n",
            "Precision: 0.0\n",
            "Recall: 0.0\n",
            "\n",
            "Class: random\n",
            "Precision: 0.0\n",
            "Recall: 0.0\n",
            "\n",
            "Class: music\n",
            "Precision: 0.0\n",
            "Recall: 0.0\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kMiTUoOHB-L5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}